dsh-client-masquerade
DeepSeek Harness plugin: make a custom llm-pi-ai provider route masquerade as Claude Code / Codex — spoofed client-identity headers, plus an opt-in request-body fingerprint for relays that gate on the body.
357 results
DeepSeek Harness plugin: make a custom llm-pi-ai provider route masquerade as Claude Code / Codex — spoofed client-identity headers, plus an opt-in request-body fingerprint for relays that gate on the body.
Give DeepSeek (text-only) models vision in Claude Code, Codex, and Agent Plugins clients: describe images via any OpenAI-compatible vision endpoint.
Claude-style skin for the dsh web GUI — warm-black canvas, clay accent, Anthropic Sans UI / Serif body font modes, follows the native light/dark theme
Embedded C/C++ firmware development toolbox — 4 agents, 8 skills covering FreeRTOS, ISR, NVM storage, Keil MDK, ARMCLANG, HardFault, state machines, architecture, LVGL patterns, and claim fact-checking. Ships a native DeepSeek Harness (dsh) bundle that injects the session-start gate into the first model step.
DSH 完整移植版 dietrichgebert/ponytail — 懒惰 senior 模式,6 个中文 Skill,无空 tool
Reduce large agent tool output by what it means, not by where it was cut.
Ponytail, lazy senior dev mode, for DeepSeek Harness: 6 skills (ponytail, ponytail-audit, ponytail-debt, ponytail-gain, ponytail-help, ponytail-review) adapted from DietrichGebert/ponytail (MIT)
TaskPack: an open, offline task-handoff container. Task Passport is the durable state; TaskPack is the box it travels in.
Bridge Claude Code's memory, skills, and configuration into DeepSeek Harness — zero migration, full compatibility
AI-for-Science research workflow skill bundle for DeepSeek Harness: multi-angle literature review with per-angle files, anchored experiment design with a caveat list, figures, paper reading; cross-verification briefs.
The standard way to install Claude Code / Codex / Cursor / Kimi plugin marketplace suites in DeepSeek Harness (DSH) — zero conversion, zero file copying. Skills, MCP servers, hooks and slash commands are injected into your dsh sessions at runtime.
Unofficial, high-fidelity Claude Code-style TUI for DeepSeek Harness, verified against real PTY captures
DeepSeek Harness (DSH) bundle porting Matt Pocock's 'Engineering for Real Engineers' + 'Productivity' Claude-Code skills (SKILL.md set) into a native DSH skill plugin. Same skill bodies, DSH skill discovery.
A DeepSeek Harness plugin that bridges projects configured for other coding agents (Claude Code, Codex, opencode, CodeBuddy Code, pi, Gemini CLI, Cursor) into DeepSeek Harness
DeepSeek Harness 插件:导入 claude-code / codex / reasonix / zcode 的聊天记录为 dsh 会话(含工作区绑定)。
Claude Code outputStyles-equivalent runtime output-style switching for DeepSeek Harness
DSH peer link — point-to-point messaging between dsh agents and other local agent sessions (e.g. Claude Code) over unix sockets. Independent plugin: register as a peer, receive messages into agent context, reply with peer_send, list peers with peer_list.
Unified agent memory for DeepSeek Harness — one shared Obsidian vault for every agent (dsh, Codex, Claude, Hermes). Ships the dependency-free Python core (search/promote/adjudicate/forget), a vault template, and a dsh plugin with memory_search / memory_show / memory_submit / memory_status tools. · 统一 Agent 记忆系统:多 Agent 共享一个 Obsidian vault,附零依赖 Python core 与 dsh 插件。
Stable, editable PPTX generation for AI agents — semantic IR in, native DrawingML out
Run the local Claude Code CLI as a first-class main conversation inside DeepSeek Harness
Modular AI research/engineering skill pack for DeepSeek Harness — security audit, paper workflows, dev workflow, storage analysis. Installable via dsh plugin add.
DSH 免费网页搜索 provider + 设置开关(Parallel 默认 / Exa 备用)+ MCP server 双传输(stdio + HTTP/SSE),兼容 Claude Code / Codex,一键切换官方计费搜索与免费搜索
AI-assisted IC design flow skills — specification, RTL, lint/CDC, simulation, synthesis, timing analysis, and signoff
Tuning Engines CLI, MCP server, and Python agent runtime adapters for governed model, agent, skill, and MCP workflows. Fine-tune open-source LLMs, run inference, manage datasets/evaluations, and connect LangGraph or Temporal while Tuning Engines handles policy, audit, usage, and token economics.